摘要
对于具有未建模动态机械臂系统,基于神经网络对非线性函数的拟合特性,用径向基神经网络来补偿机械臂系统中的未建模动态,同时提出的自适应神经网络控制器,可以保证具有未建模动态的非线性机械臂系统的渐进稳定特性。仿真结果验证了这种控制策略的有效性。
Unmodeled dynamics is the unavoidable nonlinear effect that can limit control performance in robotic systems.Meanwhile,it is not available for the control.Based on universal approximation results for radial basis function neural networks(RBF-NN),it has been proposed as an alternative to RBF-NN for approximating arbitrary nonlinear functions.Adaptive RBF neural network is used to design a compensator for unmodeled dynamics in robotic system.Then asymptotically stability of the system is assured by combining nominal feedback controller and adaptive law of NN.The simulation results show the validity of the control scheme.
出处
《仪器仪表学报》
EI
CAS
CSCD
北大核心
2006年第z1期853-855,共3页
Chinese Journal of Scientific Instrument
关键词
未建模动态
神经网络控制
函数拟合
unmodeled dynamics neural network controller function approximation
